Harvey A, Koopman S J
Department of Statistics, London School of Economics, UK.
Stat Methods Med Res. 1996 Mar;5(1):23-49. doi: 10.1177/096228029600500103.
Structural time series models are formulated in terms of components, such as trends, seasonals and cycles, which have a direct interpretation. This article describes such models and gives examples of how they can be applied in medicine. Univariate models are considered first, and then extended to include explanatory variables and interventions. Multivariate models are then shown to provide a framework for modelling longitudinal data and for carrying out intervention analysis with control groups. The final sections deal with data irregularities and non-Gaussian observations.
结构时间序列模型是根据具有直接解释的成分(如趋势、季节性和周期性)来构建的。本文描述了此类模型,并给出了它们在医学中应用的示例。首先考虑单变量模型,然后将其扩展以纳入解释变量和干预措施。接着展示多变量模型如何为纵向数据建模以及对对照组进行干预分析提供一个框架。最后几节讨论数据不规则性和非高斯观测值。